Human-level concept learning through probabilistic program induction

@article{Lake2015HumanlevelCL,
  title={Human-level concept learning through probabilistic program induction},
  author={Brenden M. Lake and Ruslan R. Salakhutdinov and Joshua B. Tenenbaum},
  journal={Science},
  year={2015},
  volume={350},
  pages={1332-1338}
}
People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms—for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world’s alphabets… CONTINUE READING
BETA

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.

809 Citations

01002003002016201720182019
Citations per Year
Semantic Scholar estimates that this publication has 809 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.

Foundations Trends Comput

  • D. Mumford
  • Graphics Vision
  • 2006

Similar Papers

Loading similar papers…